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Bankapur S, Patil N. An Effective Multi-Label Protein Sub-Chloroplast Localization Prediction by Skipped-Grams of Evolutionary Profiles Using Deep Neural Network. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:1449-1458. [PMID: 33175683 DOI: 10.1109/tcbb.2020.3037465] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Chloroplast is one of the most classic organelles in algae and plant cells. Identifying the locations of chloroplast proteins in the chloroplast organelle is an important as well as a challenging task in deciphering their functions. Biological-based experiments to identify the Protein Sub-Chloroplast Localization (PSCL) is time-consuming and cost-intensive. Over the last decade, a few computational methods have been developed to predict PSCL in which earlier works assumed to predict only single-location; whereas, recent works are able to predict multiple-locations of chloroplast organelle. However, the performances of all the state-of-the-art predictors are poor. This article proposes a novel skip-gram technique to extract highly discriminating patterns from evolutionary profiles and a multi-label deep neural network to predict the PSCL. The proposed model is assessed on two publicly available datasets, i.e., Benchmark and Novel. Experimental results demonstrate that the proposed work outperforms significantly when compared to the state-of-the-art multi-label PSCL predictors. A multi-label prediction accuracy (i.e., Overall Actual Accuracy) of the proposed model is enhanced by an absolute minimum margin of 6.7 percent on Benchmark dataset and 7.9 percent on Novel dataset when compared to the best PSCL predictor from the literature. Further, result of statistical t-test concludes that the performance of the proposed work is significantly improved and thus, the proposed work is an effective computational model to solve multi-label PSCL prediction. The proposed prediction model is hosted on web-server and available at https://nitkit-vgst727-nppsa.nitk.ac.in/deeplocpred/.
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Liu T, Chen J, Zhang Q, Hippe K, Hunt C, Le T, Cao R, Tang H. The Development of Machine Learning Methods in discriminating Secretory Proteins of Malaria Parasite. Curr Med Chem 2021; 29:807-821. [PMID: 34636289 DOI: 10.2174/0929867328666211005140625] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 07/28/2021] [Accepted: 08/15/2021] [Indexed: 11/22/2022]
Abstract
Malaria caused by Plasmodium falciparum is one of the major infectious diseases in the world. It is essential to exploit an effective method to predict secretory proteins of malaria parasites to develop effective cures and treatment. Biochemical assays can provide details for accurate identification of the secretory proteins, but these methods are expensive and time-consuming. In this paper, we summarized the machine learning-based identification algorithms and compared the construction strategies between different computational methods. Also, we discussed the use of machine learning to improve the ability of algorithms to identify proteins secreted by malaria parasites.
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Affiliation(s)
- Ting Liu
- School of Basic Medical Sciences, Southwest Medical University, Luzhou. China
| | - Jiamao Chen
- School of Basic Medical Sciences, Southwest Medical University, Luzhou. China
| | - Qian Zhang
- School of Basic Medical Sciences, Southwest Medical University, Luzhou. China
| | - Kyle Hippe
- Department of Computer Science, Pacific Lutheran University. United States
| | - Cassandra Hunt
- Department of Computer Science, Pacific Lutheran University. United States
| | - Thu Le
- Department of Computer Science, Pacific Lutheran University. United States
| | - Renzhi Cao
- Department of Computer Science, Pacific Lutheran University. United States
| | - Hua Tang
- School of Basic Medical Sciences, Southwest Medical University, Luzhou. China
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3
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Zhang S, Qiao H. KD-KLNMF: Identification of lncRNAs subcellular localization with multiple features and nonnegative matrix factorization. Anal Biochem 2020; 610:113995. [PMID: 33080214 DOI: 10.1016/j.ab.2020.113995] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2020] [Revised: 09/07/2020] [Accepted: 10/12/2020] [Indexed: 12/18/2022]
Abstract
Long non-coding RNAs (lncRNAs) refer to functional RNA molecules with a length more than 200 nucleotides and have minimal or no function to encode proteins. In recent years, more studies show that lncRNAs subcellular localization has valuable clues for their biological functions. So it is count for much to identify lncRNAs subcellular localization. In this paper, a novel statistical model named KD-KLNMF is constructed to predict lncRNAs subcellular localization. Firstly, k-mer and dinucleotide-based spatial autocorrelation are incorporated as the feature vector. Then, Synthetic Minority Over-sampling Technique is used to deal with the imbalance dataset. Next, Kullback-Leibler divergence-based nonnegative matrix factorization is applied to select optimal features. And then we utilize support vector machine as the classifier after comparing with other classifiers. Finally, the jackknife test is performed to evaluate the model. The overall accuracies reach 97.24% and 92.86% on training dataset and independent dataset, respectively. The results are better than the previous methods, which indicate that our model will be a useful and feasible tool to identify lncRNAs subcellular localization. The datasets and source code are freely available at https://github.com/HuijuanQiao/KD-KLNMF.
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Affiliation(s)
- Shengli Zhang
- School of Mathematics and Statistics, Xidian University, Xi'an, 710071, PR China.
| | - Huijuan Qiao
- School of Mathematics and Statistics, Xidian University, Xi'an, 710071, PR China
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4
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Identifying Heat Shock Protein Families from Imbalanced Data by Using Combined Features. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2020; 2020:8894478. [PMID: 33029195 PMCID: PMC7530508 DOI: 10.1155/2020/8894478] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 09/08/2020] [Accepted: 09/14/2020] [Indexed: 11/29/2022]
Abstract
Heat shock proteins (HSPs) are ubiquitous in living organisms. HSPs are an essential component for cell growth and survival; the main function of HSPs is controlling the folding and unfolding process of proteins. According to molecular function and mass, HSPs are categorized into six different families: HSP20 (small HSPS), HSP40 (J-proteins), HSP60, HSP70, HSP90, and HSP100. In this paper, improved methods for HSP prediction are proposed—the split amino acid composition (SAAC), the dipeptide composition (DC), the conjoint triad feature (CTF), and the pseudoaverage chemical shift (PseACS) were selected to predict the HSPs with a support vector machine (SVM). In order to overcome the imbalance data classification problems, the syntactic minority oversampling technique (SMOTE) was used to balance the dataset. The overall accuracy was 99.72% with a balanced dataset in the jackknife test by using the optimized combination feature SAAC+DC+CTF+PseACS, which was 4.81% higher than the imbalanced dataset with the same combination feature. The Sn, Sp, Acc, and MCC of HSP families in our predictive model were higher than those in existing methods. This improved method may be helpful for protein function prediction.
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5
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Yuan F, Liu G, Yang X, Wang S, Wang X. Prediction of oxidoreductase subfamily classes based on RFE-SND-CC-PSSM and machine learning methods. J Bioinform Comput Biol 2020; 17:1950029. [PMID: 31617464 DOI: 10.1142/s021972001950029x] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Oxidoreductase is an enzyme that widely exists in organisms. It plays an important role in cellular energy metabolism and biotransformation processes. Oxidoreductases have many subclasses with different functions, creating an important classification task in bioinformatics. In this paper, a dataset of 2640 oxidoreductase sequences was used to perform an analysis and comparison. The idea of dipeptides was introduced to process the Position Specific Score Matrix (PSSM), since each dipeptide consists of two amino acids and each column of PSSM corresponds to the information of one amino acid. Two kinds of dipeptide scores were proposed, the Standardization Normal Distribution PSSM (SND-PSSM) and the Correlation Coefficient PSSM (CC-PSSM). Recursive Feature Elimination (RFE) is used to extract features from the SND-PSSM and CC-PSSM, and the two sets of extracted features are combined to form a new feature matrix, the RFE-SND-CC-PSSM. The results show that, with the proposed method and a kernel-based nonlinear SVM classifier, the accuracy can reach 95.56% by the Jackknife test. Our method greatly improves the accuracy of oxidoreductase subclass prediction. Using this method to predict the categories of the 6 major types of enzymes effectively improves its prediction accuracy to 94.54%, indicating that this method has general applicability to other protein problems. The results show that our method is effective and universally applicable, and might be complementary to the existing methods.
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Affiliation(s)
- Fang Yuan
- Department of Biochemistry and Molecular Biology, School of Basic Medicine, Kunming Medical University, Kunming 650500, P. R. China
| | - Gan Liu
- Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming 650504, P. R. China
| | - Xiwen Yang
- Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming 650504, P. R. China
| | - Shunfang Wang
- Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming 650504, P. R. China
| | - Xueren Wang
- School of Mathematics and Statistics, Yunnan University, Kunming 650504, P. R. China
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6
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Use Chou’s 5-steps rule to identify DNase I hypersensitive sites via dinucleotide property matrix and extreme gradient boosting. Mol Genet Genomics 2020; 295:1431-1442. [DOI: 10.1007/s00438-020-01711-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Accepted: 07/11/2020] [Indexed: 01/08/2023]
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7
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Abstract
During the last three decades or so, many efforts have been made to study the protein cleavage
sites by some disease-causing enzyme, such as HIV (Human Immunodeficiency Virus) protease
and SARS (Severe Acute Respiratory Syndrome) coronavirus main proteinase. It has become increasingly
clear <i>via</i> this mini-review that the motivation driving the aforementioned studies is quite wise,
and that the results acquired through these studies are very rewarding, particularly for developing peptide
drugs.
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Affiliation(s)
- Kuo-Chen Chou
- Gordon Life Science Institute, Boston, MA 02478, United States
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8
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Li H, Du H, Wang X, Gao P, Liu Y, Lin W. Remarks on Computational Method for Identifying Acid and Alkaline Enzymes. Curr Pharm Des 2020; 26:3105-3114. [PMID: 32552636 DOI: 10.2174/1381612826666200617170826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2020] [Accepted: 05/07/2020] [Indexed: 11/22/2022]
Abstract
The catalytic efficiency of the enzyme is thousands of times higher than that of ordinary catalysts. Thus, they are widely used in industrial and medical fields. However, enzymes with protein structure can be destroyed and inactivated in high temperature, over acid or over alkali environment. It is well known that most of enzymes work well in an environment with pH of 6-8, while some special enzymes remain active only in an alkaline environment with pH > 8 or an acidic environment with pH < 6. Therefore, the identification of acidic and alkaline enzymes has become a key task for industrial production. Because of the wide varieties of enzymes, it is hard work to determine the acidity and alkalinity of the enzyme by experimental methods, and even this task cannot be achieved. Converting protein sequences into digital features and building computational models can efficiently and accurately identify the acidity and alkalinity of enzymes. This review summarized the progress of the digital features to express proteins and computational methods to identify acidic and alkaline enzymes. We hope that this paper will provide more convenience, ideas, and guides for computationally classifying acid and alkaline enzymes.
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Affiliation(s)
- Hongfei Li
- School of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China
| | - Haoze Du
- Department of Computer Science, Wake Forest University, Winston-Salem, NC, 27109, United States
| | - Xianfang Wang
- School of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China
| | - Peng Gao
- School of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China
| | - Yifeng Liu
- School of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China
| | - Weizhong Lin
- Department of Computer Science, University of Missouri, Columbia, MO, 65211, United States
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10
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Li HF, Wang XF, Tang H. Predicting Bacteriophage Enzymes and Hydrolases by Using Combined Features. Front Bioeng Biotechnol 2020; 8:183. [PMID: 32266225 PMCID: PMC7105632 DOI: 10.3389/fbioe.2020.00183] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2020] [Accepted: 02/24/2020] [Indexed: 12/19/2022] Open
Abstract
Bacteriophage is a type of virus that could infect the host bacteria. They have been applied in the treatment of pathogenic bacterial infection. Phage enzymes and hydrolases play the most important role in the destruction of bacterial cells. Correctly identifying the hydrolases coded by phage is not only beneficial to their function study, but also conducive to antibacteria drug discovery. Thus, this work aims to recognize the enzymes and hydrolases in phage. A combination of different features was used to represent samples of phage and hydrolase. A feature selection technique called analysis of variance was developed to optimize features. The classification was performed by using support vector machine (SVM). The prediction process includes two steps. The first step is to identify phage enzymes. The second step is to determine whether a phage enzyme is hydrolase or not. The jackknife cross-validated results showed that our method could produce overall accuracies of 85.1 and 94.3%, respectively, for the two predictions, demonstrating that the proposed method is promising.
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Affiliation(s)
- Hong-Fei Li
- Department of Pathophysiology, Key Laboratory of Medical Electrophysiology, Ministry of Education, Southwest Medical University, Luzhou, China.,School of Computer and Information Engineering, Henan Normal University, Henan, China
| | - Xian-Fang Wang
- School of Computer and Information Engineering, Henan Normal University, Henan, China
| | - Hua Tang
- Department of Pathophysiology, Key Laboratory of Medical Electrophysiology, Ministry of Education, Southwest Medical University, Luzhou, China
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11
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Some illuminating remarks on molecular genetics and genomics as well as drug development. Mol Genet Genomics 2020; 295:261-274. [PMID: 31894399 DOI: 10.1007/s00438-019-01634-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Accepted: 12/05/2019] [Indexed: 02/07/2023]
Abstract
Facing the explosive growth of biological sequences unearthed in the post-genomic age, one of the most important but also most difficult problems in computational biology is how to express a biological sequence with a discrete model or a vector, but still keep it with considerable sequence-order information or its special pattern. To deal with such a challenging problem, the ideas of "pseudo amino acid components" and "pseudo K-tuple nucleotide composition" have been proposed. The ideas and their approaches have further stimulated the birth for "distorted key theory", "wenxing diagram", and substantially strengthening the power in treating the multi-label systems, as well as the establishment of the famous "5-steps rule". All these logic developments are quite natural that are very useful not only for theoretical scientists but also for experimental scientists in conducting genetics/genomics analysis and drug development. Presented in this review paper are also their future perspectives; i.e., their impacts will become even more significant and propounding.
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12
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Shao YT, Liu XX, Lu Z, Chou KC. pLoc_Deep-mHum: Predict Subcellular Localization of Human Proteins by Deep Learning. ACTA ACUST UNITED AC 2020. [DOI: 10.4236/ns.2020.127042] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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13
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Shao Y, Chou KC. pLoc_Deep-mEuk: Predict Subcellular Localization of Eukaryotic Proteins by Deep Learning. ACTA ACUST UNITED AC 2020. [DOI: 10.4236/ns.2020.126034] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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14
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pLoc_bal-mHum: Predict subcellular localization of human proteins by PseAAC and quasi-balancing training dataset. Genomics 2019; 111:1274-1282. [DOI: 10.1016/j.ygeno.2018.08.007] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2018] [Revised: 08/14/2018] [Accepted: 08/16/2018] [Indexed: 12/17/2022]
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15
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Chou KC. Advances in Predicting Subcellular Localization of Multi-label Proteins and its Implication for Developing Multi-target Drugs. Curr Med Chem 2019; 26:4918-4943. [PMID: 31060481 DOI: 10.2174/0929867326666190507082559] [Citation(s) in RCA: 78] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Revised: 01/29/2019] [Accepted: 01/31/2019] [Indexed: 12/16/2022]
Abstract
The smallest unit of life is a cell, which contains numerous protein molecules. Most
of the functions critical to the cell’s survival are performed by these proteins located in its different
organelles, usually called ‘‘subcellular locations”. Information of subcellular localization
for a protein can provide useful clues about its function. To reveal the intricate pathways at the
cellular level, knowledge of the subcellular localization of proteins in a cell is prerequisite.
Therefore, one of the fundamental goals in molecular cell biology and proteomics is to determine
the subcellular locations of proteins in an entire cell. It is also indispensable for prioritizing
and selecting the right targets for drug development. Unfortunately, it is both timeconsuming
and costly to determine the subcellular locations of proteins purely based on experiments.
With the avalanche of protein sequences generated in the post-genomic age, it is highly
desired to develop computational methods for rapidly and effectively identifying the subcellular
locations of uncharacterized proteins based on their sequences information alone. Actually,
considerable progresses have been achieved in this regard. This review is focused on those
methods, which have the capacity to deal with multi-label proteins that may simultaneously
exist in two or more subcellular location sites. Protein molecules with this kind of characteristic
are vitally important for finding multi-target drugs, a current hot trend in drug development.
Focused in this review are also those methods that have use-friendly web-servers established so
that the majority of experimental scientists can use them to get the desired results without the
need to go through the detailed mathematics involved.
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Affiliation(s)
- Kuo-Chen Chou
- Gordon Life Science Institute, Boston, MA 02478, United States
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16
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Abstract
The smallest unit of life is a cell, which contains numerous protein molecules. Most
of the functions critical to the cell’s survival are performed by these proteins located in its different
organelles, usually called ‘‘subcellular locations”. Information of subcellular localization
for a protein can provide useful clues about its function. To reveal the intricate pathways at the
cellular level, knowledge of the subcellular localization of proteins in a cell is prerequisite.
Therefore, one of the fundamental goals in molecular cell biology and proteomics is to determine
the subcellular locations of proteins in an entire cell. It is also indispensable for prioritizing
and selecting the right targets for drug development. Unfortunately, it is both timeconsuming
and costly to determine the subcellular locations of proteins purely based on experiments.
With the avalanche of protein sequences generated in the post-genomic age, it is highly
desired to develop computational methods for rapidly and effectively identifying the subcellular
locations of uncharacterized proteins based on their sequences information alone. Actually,
considerable progresses have been achieved in this regard. This review is focused on those
methods, which have the capacity to deal with multi-label proteins that may simultaneously
exist in two or more subcellular location sites. Protein molecules with this kind of characteristic
are vitally important for finding multi-target drugs, a current hot trend in drug development.
Focused in this review are also those methods that have use-friendly web-servers established so
that the majority of experimental scientists can use them to get the desired results without the
need to go through the detailed mathematics involved.
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Affiliation(s)
- Kuo-Chen Chou
- Gordon Life Science Institute, Boston, MA 02478, United States
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17
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Li SH, Guan ZX, Zhang D, Zhang ZM, Huang J, Yang W, Lin H. Recent Advancement in Predicting Subcellular Localization of Mycobacterial Protein with Machine Learning Methods. Med Chem 2019; 16:605-619. [PMID: 31584379 DOI: 10.2174/1573406415666191004101913] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2019] [Revised: 06/25/2019] [Accepted: 08/23/2019] [Indexed: 01/28/2023]
Abstract
Mycobacterium tuberculosis (MTB) can cause the terrible tuberculosis (TB), which is reported as one of the most dreadful epidemics. Although many biochemical molecular drugs have been developed to cope with this disease, the drug resistance-especially the multidrug-resistant (MDR) and extensively drug-resistance (XDR)-poses a huge threat to the treatment. However, traditional biochemical experimental method to tackle TB is time-consuming and costly. Benefited by the appearance of the enormous genomic and proteomic sequence data, TB can be treated via sequence-based biological computational approach-bioinformatics. Studies on predicting subcellular localization of mycobacterial protein (MBP) with high precision and efficiency may help figure out the biological function of these proteins and then provide useful insights for protein function annotation as well as drug design. In this review, we reported the progress that has been made in computational prediction of subcellular localization of MBP including the following aspects: 1) Construction of benchmark datasets. 2) Methods of feature extraction. 3) Techniques of feature selection. 4) Application of several published prediction algorithms. 5) The published results. 6) The further study on prediction of subcellular localization of MBP.
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Affiliation(s)
- Shi-Hao Li
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zheng-Xing Guan
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Dan Zhang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zi-Mei Zhang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jian Huang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Wuritu Yang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.,Development and Planning Department, Inner Mongolia University, Hohhot, P.R. China
| | - Hao Lin
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
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18
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Identifying DNase I hypersensitive sites using multi-features fusion and F-score features selection via Chou's 5-steps rule. Biophys Chem 2019; 253:106227. [DOI: 10.1016/j.bpc.2019.106227] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Revised: 07/04/2019] [Accepted: 07/10/2019] [Indexed: 01/12/2023]
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19
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Chou KC. Proposing Pseudo Amino Acid Components is an Important Milestone for Proteome and Genome Analyses. Int J Pept Res Ther 2019. [DOI: 10.1007/s10989-019-09910-7] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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20
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21
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Xiao X, Cheng X, Chen G, Mao Q, Chou KC. pLoc_bal-mVirus: Predict Subcellular Localization of Multi-Label Virus Proteins by Chou's General PseAAC and IHTS Treatment to Balance Training Dataset. Med Chem 2019; 15:496-509. [DOI: 10.2174/1573406415666181217114710] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Revised: 10/23/2018] [Accepted: 12/12/2018] [Indexed: 12/17/2022]
Abstract
Background/Objective:Knowledge of protein subcellular localization is vitally important for both basic research and drug development. Facing the avalanche of protein sequences emerging in the post-genomic age, it is urgent to develop computational tools for timely and effectively identifying their subcellular localization based on the sequence information alone. Recently, a predictor called “pLoc-mVirus” was developed for identifying the subcellular localization of virus proteins. Its performance is overwhelmingly better than that of the other predictors for the same purpose, particularly in dealing with multi-label systems in which some proteins, known as “multiplex proteins”, may simultaneously occur in, or move between two or more subcellular location sites. Despite the fact that it is indeed a very powerful predictor, more efforts are definitely needed to further improve it. This is because pLoc-mVirus was trained by an extremely skewed dataset in which some subset was over 10 times the size of the other subsets. Accordingly, it cannot avoid the biased consequence caused by such an uneven training dataset.Methods:Using the Chou's general PseAAC (Pseudo Amino Acid Composition) approach and the IHTS (Inserting Hypothetical Training Samples) treatment to balance out the training dataset, we have developed a new predictor called “pLoc_bal-mVirus” for predicting the subcellular localization of multi-label virus proteins.Results:Cross-validation tests on exactly the same experiment-confirmed dataset have indicated that the proposed new predictor is remarkably superior to pLoc-mVirus, the existing state-of-theart predictor for the same purpose.Conclusion:Its user-friendly web-server is available at http://www.jci-bioinfo.cn/pLoc_balmVirus/, by which the majority of experimental scientists can easily get their desired results without the need to go through the detailed complicated mathematics. Accordingly, pLoc_bal-mVirus will become a very useful tool for designing multi-target drugs and in-depth understanding of the biological process in a cell.
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Affiliation(s)
- Xuan Xiao
- Gordon Life Science Institute, Boston, MA 02478, United States
| | - Xiang Cheng
- Gordon Life Science Institute, Boston, MA 02478, United States
| | - Genqiang Chen
- College of Chemistry, Chemical Engineering and Biotechnology, Donghua University, Shanghai 201620, China
| | - Qi Mao
- College of Information Science and Technology, Donghua University, Shanghai, China
| | - Kuo-Chen Chou
- Gordon Life Science Institute, Boston, MA 02478, United States
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22
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Chou KC, Cheng X, Xiao X. pLoc_bal-mEuk: Predict Subcellular Localization of Eukaryotic Proteins by General PseAAC and Quasi-balancing Training Dataset. Med Chem 2019; 15:472-485. [DOI: 10.2174/1573406415666181218102517] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Revised: 10/23/2018] [Accepted: 12/12/2018] [Indexed: 12/24/2022]
Abstract
<P>Background/Objective: Information of protein subcellular localization is crucially important for both basic research and drug development. With the explosive growth of protein sequences discovered in the post-genomic age, it is highly demanded to develop powerful bioinformatics tools for timely and effectively identifying their subcellular localization purely based on the sequence information alone. Recently, a predictor called “pLoc-mEuk” was developed for identifying the subcellular localization of eukaryotic proteins. Its performance is overwhelmingly better than that of the other predictors for the same purpose, particularly in dealing with multi-label systems where many proteins, called “multiplex proteins”, may simultaneously occur in two or more subcellular locations. Although it is indeed a very powerful predictor, more efforts are definitely needed to further improve it. This is because pLoc-mEuk was trained by an extremely skewed dataset where some subset was about 200 times the size of the other subsets. Accordingly, it cannot avoid the biased consequence caused by such an uneven training dataset. </P><P> Methods: To alleviate such bias, we have developed a new predictor called pLoc_bal-mEuk by quasi-balancing the training dataset. Cross-validation tests on exactly the same experimentconfirmed dataset have indicated that the proposed new predictor is remarkably superior to pLocmEuk, the existing state-of-the-art predictor in identifying the subcellular localization of eukaryotic proteins. It has not escaped our notice that the quasi-balancing treatment can also be used to deal with many other biological systems. </P><P> Results: To maximize the convenience for most experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/pLoc_bal-mEuk/. </P><P> Conclusion: It is anticipated that the pLoc_bal-Euk predictor holds very high potential to become a useful high throughput tool in identifying the subcellular localization of eukaryotic proteins, particularly for finding multi-target drugs that is currently a very hot trend trend in drug development.</P>
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Affiliation(s)
- Kuo-Chen Chou
- Gordon Life Science Institute, Boston, MA 02478, United States
| | - Xiang Cheng
- Gordon Life Science Institute, Boston, MA 02478, United States
| | - Xuan Xiao
- Gordon Life Science Institute, Boston, MA 02478, United States
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Liang Y, Zhang S. iDHS-DMCAC: identifying DNase I hypersensitive sites with balanced dinucleotide-based detrending moving-average cross-correlation coefficient. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2019; 30:429-445. [PMID: 31117818 DOI: 10.1080/1062936x.2019.1615546] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
DNase I hypersensitive sites (DHSs) are associated with regulatory DNA elements, so their good understanding is significant for both the biomedical research and the discovery of new drugs. Traditional experimental methods are laborious, time consuming and an inaccurately task to detect DHSs. More importantly, with the avalanche of genome sequences in the postgenomic age, it is highly essential to develop cost-effective computational approaches to identify DHSs. In this paper, we develop a statistical feature extraction model using the detrended moving-average cross-correlation (DMCA) coefficient descriptor based on dinucleotide property matrix generated by the 15 DNA dinucleotide properties, and this model is named iDHS-DMCAC. A 105-dimensional feature vector is constructed for a certain window on the two class imbalanced benchmark datasets, with over-sampling and support vector machine algorithms. Rigorous cross-validations indicate that our predictor remarkably outperforms the existing models in both accuracy and stability. We anticipate that iDHS-DMCAC will become a very useful high throughput tool, or at the very least, a complementary tool to the existing methods of identifying DNase I hypersensitive sites. The datasets and source codes of the proposed model are freely available at https://github.com/shengli0201/Datasets .
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Affiliation(s)
- Y Liang
- a School of Science , Xi'an Polytechnic University , Xi'an , P. R. China
| | - S Zhang
- b School of Mathematics and Statistics , Xidian University , Xi'an , P. R. China
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24
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Cheng X, Xiao X, Chou KC. pLoc_bal-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by quasi-balancing training dataset and general PseAAC. J Theor Biol 2018; 458:92-102. [DOI: 10.1016/j.jtbi.2018.09.005] [Citation(s) in RCA: 65] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2018] [Revised: 09/05/2018] [Accepted: 09/07/2018] [Indexed: 01/03/2023]
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25
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Liang Y, Zhang S. Identify Gram-negative bacterial secreted protein types by incorporating different modes of PSSM into Chou’s general PseAAC via Kullback–Leibler divergence. J Theor Biol 2018; 454:22-29. [DOI: 10.1016/j.jtbi.2018.05.035] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2018] [Revised: 05/19/2018] [Accepted: 05/29/2018] [Indexed: 12/14/2022]
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26
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Cheng X, Lin WZ, Xiao X, Chou KC. pLoc_bal-mAnimal: predict subcellular localization of animal proteins by balancing training dataset and PseAAC. Bioinformatics 2018; 35:398-406. [DOI: 10.1093/bioinformatics/bty628] [Citation(s) in RCA: 79] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2018] [Accepted: 07/11/2018] [Indexed: 12/25/2022] Open
Affiliation(s)
- Xiang Cheng
- Computer Science, Jingdezhen Ceramic Institute, Jingdezhen, China
- Computational Biology, Gordon Life Science Institute, Boston, MA, USA
| | - Wei-Zhong Lin
- Computer Science, Jingdezhen Ceramic Institute, Jingdezhen, China
| | - Xuan Xiao
- Computer Science, Jingdezhen Ceramic Institute, Jingdezhen, China
- Computational Biology, Gordon Life Science Institute, Boston, MA, USA
| | - Kuo-Chen Chou
- Computational Biology, Gordon Life Science Institute, Boston, MA, USA
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
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27
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Liang Y, Zhang S, Ding S. Accurate prediction of Gram-negative bacterial secreted protein types by fusing multiple statistical features from PSI-BLAST profile. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2018; 29:469-481. [PMID: 29688029 DOI: 10.1080/1062936x.2018.1459835] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2018] [Accepted: 03/27/2018] [Indexed: 06/08/2023]
Abstract
Gram-negative bacterial secreted proteins play different roles in invaded eukaryotic cells and cause various diseases. Prediction of Gram-negative bacterial secreted protein types is a meaningful and challenging task. In this paper, we develop a multiple statistical features extraction model based on the dipeptide composition (DPC) descriptor and the detrended moving-average auto-cross-correlation analysis (DMACA) descriptor by PSI-BLAST profile. A 610-dimensional feature vector was constructed on the training set, and the feature extraction model was denoted DPC-DMACA-PSSM. A support vector machine was then selected as a classifier, and the bias-free jackknife test method was used for evaluating the accuracy. Our predictor achieves favourable performance for overall accuracy on the test set and also outperforms the other published approaches. The results show that our approach offers a reliable tool for the identification of Gram-negative bacterial secreted protein types.
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Affiliation(s)
- Y Liang
- a School of Science , Xi'an Polytechnic University , Xi'an 710048 , PR China
| | - S Zhang
- b School of Mathematics and Statistics , Xidian University , Xi'an 710071 , PR China
| | - S Ding
- c Department of Sciences , Dalian Nationalities University , Dalian 116600 , PR China
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28
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pLoc_bal-mGpos: Predict subcellular localization of Gram-positive bacterial proteins by quasi-balancing training dataset and PseAAC. Genomics 2018; 111:886-892. [PMID: 29842950 DOI: 10.1016/j.ygeno.2018.05.017] [Citation(s) in RCA: 79] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Revised: 05/14/2018] [Accepted: 05/18/2018] [Indexed: 12/12/2022]
Abstract
Knowledge of protein subcellular localization is vitally important for both basic research and drug development. With the avalanche of protein sequences emerging in the post-genomic age, it is highly desired to develop computational tools for timely and effectively identifying their subcellular localization purely based on the sequence information alone. Recently, a predictor called "pLoc-mGpos" was developed for identifying the subcellular localization of Gram-positive bacterial proteins. Its performance is overwhelmingly better than that of the other predictors for the same purpose, particularly in dealing with multi-label systems in which some proteins, called "multiplex proteins", may simultaneously occur in two or more subcellular locations. Although it is indeed a very powerful predictor, more efforts are definitely needed to further improve it. This is because pLoc-mGpos was trained by an extremely skewed dataset in which some subset (subcellular location) was over 11 times the size of the other subsets. Accordingly, it cannot avoid the bias consequence caused by such an uneven training dataset. To alleviate such bias consequence, we have developed a new and bias-reducing predictor called pLoc_bal-mGpos by quasi-balancing the training dataset. Rigorous target jackknife tests on exactly the same experiment-confirmed dataset have indicated that the proposed new predictor is remarkably superior to pLoc-mGpos, the existing state-of-the-art predictor in identifying the subcellular localization of Gram-positive bacterial proteins. To maximize the convenience for most experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/pLoc_bal-mGpos/, by which users can easily get their desired results without the need to go through the detailed mathematics.
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29
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Uddin MR, Sharma A, Farid DM, Rahman MM, Dehzangi A, Shatabda S. EvoStruct-Sub: An accurate Gram-positive protein subcellular localization predictor using evolutionary and structural features. J Theor Biol 2018; 443:138-146. [DOI: 10.1016/j.jtbi.2018.02.002] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2017] [Revised: 01/18/2018] [Accepted: 02/03/2018] [Indexed: 12/21/2022]
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30
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Liang Y, Zhang S. Prediction of Apoptosis Protein's Subcellular Localization by Fusing Two Different Descriptors Based on Evolutionary Information. Acta Biotheor 2018. [PMID: 29532347 DOI: 10.1007/s10441-018-9319-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The apoptosis protein has a central role in the development and the homeostasis of an organism. Obtaining information about the subcellular localization of apoptosis protein is very helpful to understand the apoptosis mechanism and the function of this protein. Prediction of apoptosis protein's subcellular localization is a challenging task, and currently the existing feature extraction methods mainly rely on the protein's primary sequence. In this paper we develop a feature extraction model based on two different descriptors of evolutionary information, which contains the 192 frequencies of triplet codons (FTC) in the RNA sequence derived from the protein's primary sequence and the 190 features from a detrended forward moving-average cross-correlation analysis (DFMCA) based on a position-specific scoring matrix (PSSM) generated by the PSI-BLAST program. Hence, this model is called FTC-DFMCA-PSSM. A 382-dimensional (382D) feature vector is constructed on the ZD98, ZW225 and CL317 datasets. Then a support vector machine is adopted as classifier, and the jackknife cross-validation test method is used for evaluating the accuracy. The overall prediction accuracies are further improved by an objective and rigorous jackknife test. Our model not only broadens the source of the feature information, but also provides a more accurate and reliable automated calculation method for the prediction of apoptosis protein's subcellular localization.
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Affiliation(s)
- Yunyun Liang
- School of Science, Xi'an Polytechnic University, Xi'an, 710048, People's Republic of China.
| | - Shengli Zhang
- School of Mathematics and Statistics, Xidian University, Xi'an, 710071, People's Republic of China.
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31
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pLoc-mEuk: Predict subcellular localization of multi-label eukaryotic proteins by extracting the key GO information into general PseAAC. Genomics 2018; 110:50-58. [DOI: 10.1016/j.ygeno.2017.08.005] [Citation(s) in RCA: 180] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2017] [Revised: 08/10/2017] [Accepted: 08/11/2017] [Indexed: 11/22/2022]
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32
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PSBinder: A Web Service for Predicting Polystyrene Surface-Binding Peptides. BIOMED RESEARCH INTERNATIONAL 2017; 2017:5761517. [PMID: 29445741 PMCID: PMC5763211 DOI: 10.1155/2017/5761517] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/13/2017] [Accepted: 11/02/2017] [Indexed: 11/18/2022]
Abstract
Polystyrene surface-binding peptides (PSBPs) are useful as affinity tags to build a highly effective ELISA system. However, they are also a quite common type of target-unrelated peptides (TUPs) in the panning of phage-displayed random peptide library. As TUP, PSBP will mislead the analysis of panning results if not identified. Therefore, it is necessary to find a way to quickly and easily foretell if a peptide is likely to be a PSBP or not. In this paper, we describe PSBinder, a predictor based on SVM. To our knowledge, it is the first web server for predicting PSBP. The SVM model was built with the feature of optimized dipeptide composition and 87.02% (MCC = 0.74; AUC = 0.91) of peptides were correctly classified by fivefold cross-validation. PSBinder can be used to exclude highly possible PSBP from biopanning results or to find novel candidates for polystyrene affinity tags. Either way, it is valuable for biotechnology community.
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33
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Bi-PSSM: Position specific scoring matrix based intelligent computational model for identification of mycobacterial membrane proteins. J Theor Biol 2017; 435:116-124. [DOI: 10.1016/j.jtbi.2017.09.013] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2017] [Revised: 09/12/2017] [Accepted: 09/15/2017] [Indexed: 02/08/2023]
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34
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Accurate prediction of subcellular location of apoptosis proteins combining Chou's PseAAC and PsePSSM based on wavelet denoising. Oncotarget 2017; 8:107640-107665. [PMID: 29296195 PMCID: PMC5746097 DOI: 10.18632/oncotarget.22585] [Citation(s) in RCA: 59] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2017] [Accepted: 10/30/2017] [Indexed: 02/05/2023] Open
Abstract
Apoptosis proteins subcellular localization information are very important for understanding the mechanism of programmed cell death and the development of drugs. The prediction of subcellular localization of an apoptosis protein is still a challenging task because the prediction of apoptosis proteins subcellular localization can help to understand their function and the role of metabolic processes. In this paper, we propose a novel method for protein subcellular localization prediction. Firstly, the features of the protein sequence are extracted by combining Chou's pseudo amino acid composition (PseAAC) and pseudo-position specific scoring matrix (PsePSSM), then the feature information of the extracted is denoised by two-dimensional (2-D) wavelet denoising. Finally, the optimal feature vectors are input to the SVM classifier to predict subcellular location of apoptosis proteins. Quite promising predictions are obtained using the jackknife test on three widely used datasets and compared with other state-of-the-art methods. The results indicate that the method proposed in this paper can remarkably improve the prediction accuracy of apoptosis protein subcellular localization, which will be a supplementary tool for future proteomics research.
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35
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Liang Y, Zhang S. Predict protein structural class by incorporating two different modes of evolutionary information into Chou's general pseudo amino acid composition. J Mol Graph Model 2017; 78:110-117. [DOI: 10.1016/j.jmgm.2017.10.003] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2017] [Revised: 10/03/2017] [Accepted: 10/03/2017] [Indexed: 11/27/2022]
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36
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Wan S, Mak MW, Kung SY. Transductive Learning for Multi-Label Protein Subchloroplast Localization Prediction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2017; 14:212-224. [PMID: 26887009 DOI: 10.1109/tcbb.2016.2527657] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Predicting the localization of chloroplast proteins at the sub-subcellular level is an essential yet challenging step to elucidate their functions. Most of the existing subchloroplast localization predictors are limited to predicting single-location proteins and ignore the multi-location chloroplast proteins. While recent studies have led to some multi-location chloroplast predictors, they usually perform poorly. This paper proposes an ensemble transductive learning method to tackle this multi-label classification problem. Specifically, given a protein in a dataset, its composition-based sequence information and profile-based evolutionary information are respectively extracted. These two kinds of features are respectively compared with those of other proteins in the dataset. The comparisons lead to two similarity vectors which are weighted-combined to constitute an ensemble feature vector. A transductive learning model based on the least squares and nearest neighbor algorithms is proposed to process the ensemble features. We refer to the resulting predictor to as EnTrans-Chlo. Experimental results on a stringent benchmark dataset and a novel dataset demonstrate that EnTrans-Chlo significantly outperforms state-of-the-art predictors and particularly gains more than 4% (absolute) improvement on the overall actual accuracy. For readers' convenience, EnTrans-Chlo is freely available online at http://bioinfo.eie.polyu.edu.hk/EnTransChloServer/.
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37
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Liu B, Wu H, Chou KC. Pse-in-One 2.0: An Improved Package of Web Servers for Generating Various Modes of Pseudo Components of DNA, RNA, and Protein Sequences. ACTA ACUST UNITED AC 2017. [DOI: 10.4236/ns.2017.94007] [Citation(s) in RCA: 91] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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38
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Wei L, Liao M, Gao X, Wang J, Lin W. mGOF-loc: A novel ensemble learning method for human protein subcellular localization prediction. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2015.09.137] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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39
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Wan S, Mak MW, Kung SY. Ensemble Linear Neighborhood Propagation for Predicting Subchloroplast Localization of Multi-Location Proteins. J Proteome Res 2016; 15:4755-4762. [DOI: 10.1021/acs.jproteome.6b00686] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Shibiao Wan
- Department
of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Man-Wai Mak
- Department
of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Sun-Yuan Kung
- Department
of Electrical Engineering, Princeton University, New Jersey 08540, United States
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40
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Liang Y, Liu S, Zhang S. Detrended cross-correlation coefficient: Application to predict apoptosis protein subcellular localization. Math Biosci 2016; 282:61-67. [PMID: 27720879 DOI: 10.1016/j.mbs.2016.09.019] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2016] [Revised: 09/27/2016] [Accepted: 09/28/2016] [Indexed: 01/02/2023]
Abstract
Apoptosis, or programed cell death, plays a central role in the development and homeostasis of an organism. Obtaining information on subcellular location of apoptosis proteins is very helpful for understanding the apoptosis mechanism. The prediction of subcellular localization of an apoptosis protein is still a challenging task, and existing methods mainly based on protein primary sequences. In this paper, we introduce a new position-specific scoring matrix (PSSM)-based method by using detrended cross-correlation (DCCA) coefficient of non-overlapping windows. Then a 190-dimensional (190D) feature vector is constructed on two widely used datasets: CL317 and ZD98, and support vector machine is adopted as classifier. To evaluate the proposed method, objective and rigorous jackknife cross-validation tests are performed on the two datasets. The results show that our approach offers a novel and reliable PSSM-based tool for prediction of apoptosis protein subcellular localization.
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Affiliation(s)
- Yunyun Liang
- School of Mathematics and Statistics, Xidian University, Xi'an 710071, PR China.
| | - Sanyang Liu
- School of Mathematics and Statistics, Xidian University, Xi'an 710071, PR China
| | - Shengli Zhang
- School of Mathematics and Statistics, Xidian University, Xi'an 710071, PR China
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41
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Fan GL, Liu YL, Wang H. Identification of thermophilic proteins by incorporating evolutionary and acid dissociation information into Chou's general pseudo amino acid composition. J Theor Biol 2016; 407:138-142. [DOI: 10.1016/j.jtbi.2016.07.010] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2016] [Revised: 06/24/2016] [Accepted: 07/07/2016] [Indexed: 10/21/2022]
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42
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Li FM, Wang XQ. Identifying anticancer peptides by using improved hybrid compositions. Sci Rep 2016; 6:33910. [PMID: 27670968 PMCID: PMC5037382 DOI: 10.1038/srep33910] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2016] [Accepted: 09/02/2016] [Indexed: 12/20/2022] Open
Abstract
Cancer is one of the main causes of threats to human life. Identification of anticancer peptides is important for developing effective anticancer drugs. In this paper, we developed an improved predictor to identify the anticancer peptides. The amino acid composition (AAC), the average chemical shifts (acACS) and the reduced amino acid composition (RAAC) were selected to predict the anticancer peptides by using the support vector machine (SVM). The overall prediction accuracy reaches to 93.61% in jackknife test. The results indicated that the combined parameter was helpful to the prediction for anticancer peptides.
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Affiliation(s)
- Feng-Min Li
- College of Science, Inner Mongolia Agricultural University, Hohhot, 010018, China
| | - Xiao-Qian Wang
- College of Science, Inner Mongolia Agricultural University, Hohhot, 010018, China
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43
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Zhang L, Kong L, Han X, Lv J. Structural class prediction of protein using novel feature extraction method from chaos game representation of predicted secondary structure. J Theor Biol 2016; 400:1-10. [DOI: 10.1016/j.jtbi.2016.04.011] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2016] [Revised: 03/18/2016] [Accepted: 04/08/2016] [Indexed: 11/30/2022]
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44
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Feng Z, Hu X, Jiang Z, Song H, Ashraf MA. The recognition of multi-class protein folds by adding average chemical shifts of secondary structure elements. Saudi J Biol Sci 2016; 23:189-97. [PMID: 26980999 PMCID: PMC4778582 DOI: 10.1016/j.sjbs.2015.10.008] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2015] [Revised: 10/08/2015] [Accepted: 10/12/2015] [Indexed: 11/28/2022] Open
Abstract
The recognition of protein folds is an important step in the prediction of protein structure and function. Recently, an increasing number of researchers have sought to improve the methods for protein fold recognition. Following the construction of a dataset consisting of 27 protein fold classes by Ding and Dubchak in 2001, prediction algorithms, parameters and the construction of new datasets have improved for the prediction of protein folds. In this study, we reorganized a dataset consisting of 76-fold classes constructed by Liu et al. and used the values of the increment of diversity, average chemical shifts of secondary structure elements and secondary structure motifs as feature parameters in the recognition of multi-class protein folds. With the combined feature vector as the input parameter for the Random Forests algorithm and ensemble classification strategy, we propose a novel method to identify the 76 protein fold classes. The overall accuracy of the test dataset using an independent test was 66.69%; when the training and test sets were combined, with 5-fold cross-validation, the overall accuracy was 73.43%. This method was further used to predict the test dataset and the corresponding structural classification of the first 27-protein fold class dataset, resulting in overall accuracies of 79.66% and 93.40%, respectively. Moreover, when the training set and test sets were combined, the accuracy using 5-fold cross-validation was 81.21%. Additionally, this approach resulted in improved prediction results using the 27-protein fold class dataset constructed by Ding and Dubchak.
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Affiliation(s)
- Zhenxing Feng
- Department of Sciences, Inner Mongolia University of Technology, Hohhot, China
| | - Xiuzhen Hu
- Department of Sciences, Inner Mongolia University of Technology, Hohhot, China
| | - Zhuo Jiang
- Department of Sciences, Inner Mongolia University of Technology, Hohhot, China
| | - Hangyu Song
- Department of Sciences, Inner Mongolia University of Technology, Hohhot, China
| | - Muhammad Aqeel Ashraf
- Water Research Unit, Faculty of Science and Natural Resources, University Malaysia Sabah, 88400 Kota Kinabalu, Sabah, Malaysia
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45
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Wan S, Mak MW, Kung SY. Sparse regressions for predicting and interpreting subcellular localization of multi-label proteins. BMC Bioinformatics 2016; 17:97. [PMID: 26911432 PMCID: PMC4765148 DOI: 10.1186/s12859-016-0940-x] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2015] [Accepted: 01/27/2016] [Indexed: 11/10/2022] Open
Abstract
Background Predicting protein subcellular localization is indispensable for inferring protein functions. Recent studies have been focusing on predicting not only single-location proteins, but also multi-location proteins. Almost all of the high performing predictors proposed recently use gene ontology (GO) terms to construct feature vectors for classification. Despite their high performance, their prediction decisions are difficult to interpret because of the large number of GO terms involved. Results This paper proposes using sparse regressions to exploit GO information for both predicting and interpreting subcellular localization of single- and multi-location proteins. Specifically, we compared two multi-label sparse regression algorithms, namely multi-label LASSO (mLASSO) and multi-label elastic net (mEN), for large-scale predictions of protein subcellular localization. Both algorithms can yield sparse and interpretable solutions. By using the one-vs-rest strategy, mLASSO and mEN identified 87 and 429 out of more than 8,000 GO terms, respectively, which play essential roles in determining subcellular localization. More interestingly, many of the GO terms selected by mEN are from the biological process and molecular function categories, suggesting that the GO terms of these categories also play vital roles in the prediction. With these essential GO terms, not only where a protein locates can be decided, but also why it resides there can be revealed. Conclusions Experimental results show that the output of both mEN and mLASSO are interpretable and they perform significantly better than existing state-of-the-art predictors. Moreover, mEN selects more features and performs better than mLASSO on a stringent human benchmark dataset. For readers’ convenience, an online server called SpaPredictor for both mLASSO and mEN is available at http://bioinfo.eie.polyu.edu.hk/SpaPredictorServer/.
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Affiliation(s)
- Shibiao Wan
- Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong, SAR, China.
| | - Man-Wai Mak
- Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong, SAR, China.
| | - Sun-Yuan Kung
- Department of Electrical Engineering, Princeton University, New Jersey, USA.
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46
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Fan GL, Zhang XY, Liu YL, Nang Y, Wang H. DSPMP: Discriminating secretory proteins of malaria parasite by hybridizing different descriptors of Chou's pseudo amino acid patterns. J Comput Chem 2015; 36:2317-27. [PMID: 26484844 DOI: 10.1002/jcc.24210] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2015] [Revised: 08/20/2015] [Accepted: 08/23/2015] [Indexed: 12/28/2022]
Abstract
Identification of the proteins secreted by the malaria parasite is important for developing effective drugs and vaccines against infection. Therefore, we developed an improved predictor called "DSPMP" (Discriminating Secretory Proteins of Malaria Parasite) to identify the secretory proteins of the malaria parasite by integrating several vector features using support vector machine-based methods. DSPMP achieved an overall predictive accuracy of 98.61%, which is superior to that of the existing predictors in this field. We show that our method is capable of identifying the secretory proteins of the malaria parasite and found that the amino acid composition for buried and exposed sequences, denoted by AAC(b/e), was the most important feature for constructing the predictor. This article not only introduces a novel method for detecting the important features of sample proteins related to the malaria parasite but also provides a useful tool for tackling general protein-related problems. The DSPMP webserver is freely available at http://202.207.14.87:8032/fuwu/DSPMP/index.asp.
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Affiliation(s)
- Guo-Liang Fan
- Department of Physics, School of Physical Science and Technology, Inner Mongolia University, Hohhot, 010021, China
| | - Xiao-Yan Zhang
- Department of Physics, Inner Mongolia University of Technology, Hohhot, 010051, China
| | - Yan-Ling Liu
- Department of Physics, School of Physical Science and Technology, Inner Mongolia University, Hohhot, 010021, China
| | - Yi Nang
- Department of Physics, School of Physical Science and Technology, Inner Mongolia University, Hohhot, 010021, China
| | - Hui Wang
- Department of Physics, School of Physical Science and Technology, Inner Mongolia University, Hohhot, 010021, China
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47
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Georgiou DN, Karakasidis TE, Megaritis AC, Nieto JJ, Torres A. An extension of fuzzy topological approach for comparison of genetic sequences. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2015. [DOI: 10.3233/ifs-151701] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- DN Georgiou
- Department of Mathematics, University of Patras, Patras, Greece
| | - TE Karakasidis
- Department of Civil Engineering, University of Thessaly, Volos, Greece
| | - AC Megaritis
- Technological Educational Institute of Western Greece, Department of Accounting and Finance, Messolonghi, Greece
| | - Juan J. Nieto
- Departamento de Análisis Matemático, Facultad de Matemáticas, Universidad de Santiago de Compostela, Spain
| | - A Torres
- Departamento de Psiquiatría Radiología y Salud Pública, Facultad de Medicina, Universidad de Santiago de Compostela, Spain
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48
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Liu B, Chen J, Wang X. Protein remote homology detection by combining Chou’s distance-pair pseudo amino acid composition and principal component analysis. Mol Genet Genomics 2015; 290:1919-31. [DOI: 10.1007/s00438-015-1044-4] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2015] [Accepted: 04/06/2015] [Indexed: 02/07/2023]
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49
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Chen W, Lin H, Chou KC. Pseudo nucleotide composition or PseKNC: an effective formulation for analyzing genomic sequences. MOLECULAR BIOSYSTEMS 2015; 11:2620-34. [DOI: 10.1039/c5mb00155b] [Citation(s) in RCA: 262] [Impact Index Per Article: 29.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
With the avalanche of DNA/RNA sequences generated in the post-genomic age, it is urgent to develop automated methods for analyzing the relationship between the sequences and their functions.
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Affiliation(s)
- Wei Chen
- Department of Physics
- School of Sciences
- and Center for Genomics and Computational Biology
- Hebei United University
- Tangshan 063000
| | - Hao Lin
- Gordon Life Science Institute
- Boston
- USA
- Key Laboratory for Neuro-Information of Ministry of Education
- Center of Bioinformatics
| | - Kuo-Chen Chou
- Department of Physics
- School of Sciences
- and Center for Genomics and Computational Biology
- Hebei United University
- Tangshan 063000
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50
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Zhu PP, Li WC, Zhong ZJ, Deng EZ, Ding H, Chen W, Lin H. Predicting the subcellular localization of mycobacterial proteins by incorporating the optimal tripeptides into the general form of pseudo amino acid composition. MOLECULAR BIOSYSTEMS 2015; 11:558-63. [DOI: 10.1039/c4mb00645c] [Citation(s) in RCA: 97] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Mycobacterium tuberculosis is a bacterium that causes tuberculosis, one of the most prevalent infectious diseases.
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Affiliation(s)
- Pan-Pan Zhu
- Key Laboratory for Neuro-Information of Ministry of Education
- Center of Bioinformatics
- School of Life Science and Technology
- University of Electronic Science and Technology of China
- Chengdu 610054
| | - Wen-Chao Li
- Key Laboratory for Neuro-Information of Ministry of Education
- Center of Bioinformatics
- School of Life Science and Technology
- University of Electronic Science and Technology of China
- Chengdu 610054
| | - Zhe-Jin Zhong
- Key Laboratory for Neuro-Information of Ministry of Education
- Center of Bioinformatics
- School of Life Science and Technology
- University of Electronic Science and Technology of China
- Chengdu 610054
| | - En-Ze Deng
- Key Laboratory for Neuro-Information of Ministry of Education
- Center of Bioinformatics
- School of Life Science and Technology
- University of Electronic Science and Technology of China
- Chengdu 610054
| | - Hui Ding
- Key Laboratory for Neuro-Information of Ministry of Education
- Center of Bioinformatics
- School of Life Science and Technology
- University of Electronic Science and Technology of China
- Chengdu 610054
| | - Wei Chen
- Department of Physics
- School of Sciences
- and Center for Genomics and Computational Biology
- Hebei United University
- Tangshan 063000
| | - Hao Lin
- Key Laboratory for Neuro-Information of Ministry of Education
- Center of Bioinformatics
- School of Life Science and Technology
- University of Electronic Science and Technology of China
- Chengdu 610054
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